babf7c9f50282143ab8efee96a587bf5cb74123f,chainercv/evaluations/eval_semantic_segmentation_iou.py,,calc_semantic_segmentation_confusion,#Any#Any#Any#,6

Before Change

    gt_labels = iter(gt_labels)

    confusion = np.zeros((n_class, n_class), dtype=np.int64)
    while True:
        try:
            pred_label = next(pred_labels)
            gt_label = next(gt_labels)
        except StopIteration:
            break
        if pred_label.ndim != 2 or gt_label.ndim != 2:
            raise ValueError("ndim of inputs should be two.")
        if pred_label.shape != gt_label.shape:
            raise ValueError("Shapes of inputs should be same.")

        pred_label = pred_label.flatten()
        gt_label = gt_label.flatten()
        mask = (gt_label >= 0) & (gt_label < n_class)
        confusion += np.bincount(
            n_class * gt_label[mask].astype(int) +
            pred_label[mask], minlength=n_class**2).reshape(n_class, n_class)
    return confusion


def calc_semantic_segmentation_iou(confusion):

After Change

    gt_labels = iter(gt_labels)

    confusion = np.zeros((n_class, n_class), dtype=np.int64)
    for pred_label, gt_label in six.moves.zip(pred_labels, gt_labels):
        if pred_label.ndim != 2 or gt_label.ndim != 2:
            raise ValueError("ndim of inputs should be two.")
        if pred_label.shape != gt_label.shape:
            raise ValueError("Shapes of inputs should be same.")

        pred_label = pred_label.flatten()
        gt_label = gt_label.flatten()
        mask = (gt_label >= 0) & (gt_label < n_class)
        confusion += np.bincount(
            n_class * gt_label[mask].astype(int) +
            pred_label[mask], minlength=n_class**2).reshape(n_class, n_class)

    for iter_ in (pred_labels, gt_labels):
        // This code assumes any iterator does not contain None as its items.
        if next(iter_, None) is not None:
            raise ValueError("Length of input iterables need to be same")

Instances